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<b>Introduction</b> <p>Blind users rely on multiple strategies while searching for content on the Web, through the use of Assistive Technologies, such as screen readers. HTML5 Semantic tags and ARIA Landmarks were introduced to enhance the semantics associated with elements in the web application, providing improved navigation for disabled users. Among these components, section tags and ARIA Regions define a navigation structure for a webpage’s content, which is helpful for blind users interacting with the Web through Assistive Technologies. Accessibility specifications, such as WCAG and ARIA, recommend using this markup. However, web developers are not always aware of these technical requirements, as many web applications have been deployed without these inclusive navigation mechanisms.</p> <b>Objectives</b> <p>This paper proposes a DOM-Structure Similarity Classification and Clustering pipeline for automatically identifying ARIA Regions in web applications.</p> <b>Method</b> <p>We conducted an experiment to evaluate the number of ARIA Regions that the approach can correctly identify.</p> <b>Results</b> <p>The results showed that the approach identified significantly more ARIA Regions than our previous approach.</p> <b>Conclusion</b> <p>The experiment showed the approach’s potential for automatically segmenting web application structure in ARIA Regions, enhancing navigation mechanisms for blind users on the Web.</p>

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ARIA region landmarks identification using a DOM-structure similarity classification and clustering pipeline

  • 期刊论文

  • 发表日期：

    2025年12月24日

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  • Willian M. Watanabe,
  • Vanice Dalto,
  • André Pimenta Freire

摘要

Introduction

Blind users rely on multiple strategies while searching for content on the Web, through the use of Assistive Technologies, such as screen readers. HTML5 Semantic tags and ARIA Landmarks were introduced to enhance the semantics associated with elements in the web application, providing improved navigation for disabled users. Among these components, section tags and ARIA Regions define a navigation structure for a webpage’s content, which is helpful for blind users interacting with the Web through Assistive Technologies. Accessibility specifications, such as WCAG and ARIA, recommend using this markup. However, web developers are not always aware of these technical requirements, as many web applications have been deployed without these inclusive navigation mechanisms.

Objectives

This paper proposes a DOM-Structure Similarity Classification and Clustering pipeline for automatically identifying ARIA Regions in web applications.

Method

We conducted an experiment to evaluate the number of ARIA Regions that the approach can correctly identify.

Results

The results showed that the approach identified significantly more ARIA Regions than our previous approach.

Conclusion

The experiment showed the approach’s potential for automatically segmenting web application structure in ARIA Regions, enhancing navigation mechanisms for blind users on the Web.